{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129212"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129212","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven predictive pursuit-evasion engagement guidance and fast posture reconstruction of soft continuum arm","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Akcal, Ugur"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-17","date_published":"2025-04-17","updated_at":"2026-07-22T22:25:04Z","subjects":["Artificial Neural Networks","Data-driven Predictive Guidance","Posture Reconstruction","Soft Continuum Arm"],"languages":["en","eng"],"rights":["Copyright 2025 Ugur Akcal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129212","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Akcal, Ugur"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-17","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Neural Networks","Data-driven Predictive Guidance","Posture Reconstruction","Soft Continuum Arm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Ugur Akcal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129212"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Ugur Akcal, accepted the attached license on 2025-04-16 at 20:30.","The student, Ugur Akcal, submitted this Thesis for approval on 2025-04-16 at 20:51.","This Thesis was approved for publication on 2025-04-17 at 09:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21809 on 2025-10-19 at 18:09:29","This thesis explores artificial neural network-based methodologies designed to improve performance in two critical areas of robotics: high-precision pursuit-evasion guidance and soft continuum arm posture reconstruction. First, a predictive guidance scheme is developed to enable rapid interception of agile and evasive targets with limited knowledge of evader dynamics. A recurrent neural network is trained on representative evader maneuvers to efficiently predict future acceleration commands. These predictions are incorporated into a finite-horizon optimal control problem, generating near-optimal guidance commands that significantly outperform traditional reactive laws such as proportional navigation in dynamic engagement scenarios, particularly in terms of average miss distance. Second, the thesis introduces a framework in which the Vicon motion capture system is leveraged to acquire high-fidelity ground-truth posture data in order to train an artificial neural network for fast and smooth posture reconstruction of soft continuum arms. Given the infinite-dimensional nature of soft-arm deformation, strain fields are represented using a low-dimensional set of principal components. A feed-forward neural network is trained in an unsupervised manner with a physics-informed loss to instantly infer the coefficients for the principal components from sparse marker measurements. This approach allows for real-time posture reconstruction, achieving computation speeds five orders of magnitude faster than classical iterative or optimization-based techniques while preserving accuracy and smoothness. Together, these contributions underscore the potential of neural networks to unify control, estimation, and efficient computation in robotics. By bridging pursuit-evasion engagements and continuum robot shape reconstruction, the thesis highlights the versatility and performance gains afforded by data-driven models, ultimately paving the way for advanced, high performance robotic autonomy in both aerial and soft arm applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven predictive pursuit-evasion engagement guidance and fast posture reconstruction of soft continuum arm"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish"],"dc:creator":["Akcal, Ugur"],"dc:date":["2025-04-17","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Ugur Akcal, accepted the attached license on 2025-04-16 at 20:30.","The student, Ugur Akcal, submitted this Thesis for approval on 2025-04-16 at 20:51.","This Thesis was approved for publication on 2025-04-17 at 09:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21809 on 2025-10-19 at 18:09:29","This thesis explores artificial neural network-based methodologies designed to improve performance in two critical areas of robotics: high-precision pursuit-evasion guidance and soft continuum arm posture reconstruction. First, a predictive guidance scheme is developed to enable rapid interception of agile and evasive targets with limited knowledge of evader dynamics. A recurrent neural network is trained on representative evader maneuvers to efficiently predict future acceleration commands. These predictions are incorporated into a finite-horizon optimal control problem, generating near-optimal guidance commands that significantly outperform traditional reactive laws such as proportional navigation in dynamic engagement scenarios, particularly in terms of average miss distance. Second, the thesis introduces a framework in which the Vicon motion capture system is leveraged to acquire high-fidelity ground-truth posture data in order to train an artificial neural network for fast and smooth posture reconstruction of soft continuum arms. Given the infinite-dimensional nature of soft-arm deformation, strain fields are represented using a low-dimensional set of principal components. A feed-forward neural network is trained in an unsupervised manner with a physics-informed loss to instantly infer the coefficients for the principal components from sparse marker measurements. This approach allows for real-time posture reconstruction, achieving computation speeds five orders of magnitude faster than classical iterative or optimization-based techniques while preserving accuracy and smoothness. Together, these contributions underscore the potential of neural networks to unify control, estimation, and efficient computation in robotics. By bridging pursuit-evasion engagements and continuum robot shape reconstruction, the thesis highlights the versatility and performance gains afforded by data-driven models, ultimately paving the way for advanced, high performance robotic autonomy in both aerial and soft arm applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129212"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Ugur Akcal"],"dc:subject":["Artificial Neural Networks","Data-driven Predictive Guidance","Posture Reconstruction","Soft Continuum Arm"],"dc:title":["Data-driven predictive pursuit-evasion engagement guidance and fast posture reconstruction of soft continuum arm"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}